Article(id=1254362824688460436, tenantId=1146029695717560320, journalId=1254119036117037056, issueId=1254362823425974931, articleNumber=null, orderNo=null, doi=10.13788/j.cnki.cbgc.2026.03.03, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1755705600000, receivedDateStr=2025-08-21, revisedDate=1760716800000, revisedDateStr=2025-10-18, acceptedDate=null, acceptedDateStr=null, onlineDate=1776993002335, onlineDateStr=2026-04-24, pubDate=1774368000000, pubDateStr=2026-03-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1776993002335, onlineIssueDateStr=2026-04-24, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1776993002335, creator=13701087609, updateTime=1776993002335, updator=13701087609, issue=Issue{id=1254362823425974931, tenantId=1146029695717560320, journalId=1254119036117037056, year='2026', volume='48', issue='3', pageStart='1', pageEnd='190', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=1, specialIssue=null, createTime=1776993002036, creator=13701087609, updateTime=1776993258606, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1254363899764077151, tenantId=1146029695717560320, journalId=1254119036117037056, issueId=1254362823425974931, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1254363899764077152, tenantId=1146029695717560320, journalId=1254119036117037056, issueId=1254362823425974931, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=23, endPage=31, ext={EN=ArticleExt(id=1254362825070142103, articleId=1254362824688460436, tenantId=1146029695717560320, journalId=1254119036117037056, language=EN, title=Intelligent Ship Trajectory Prediction Based on ABiM-Ship, columnId=1254362824956895894, journalTitle=Ship Engineering, columnName=Special Topic: Intelligent Ship, runingTitle=null, highlight=null, articleAbstract=
[Purpose]

To improve the accuracy and robustness of ship trajectory prediction,

[Method]

an ABiM-Ship network that encodes historical trajectories using a bidirectional selective state space model is proposed. An attention mechanism to explicitly align trajectories with heading and speed is utilized. A two-stage end-to-end joint prediction is designed, first regressing future trajectories, heading, and speed, then refining them using residual correction. Huber loss is introduced to constrain physical errors and stabilize convergence.

[Result]

The experimental results show that this network outperforms traditional mainstream baselines in terms of average prediction error over short, medium, and long distances, achieving high prediction accuracy. The representation method, two-stage structure, and Huber loss all contribute significantly to performance gains.

[Conclusion]

The research findings achieve explicit coupling and coarse-to-fine prediction for trajectories, heading, and speed while maintaining linear temporal complexity. They have good reproducibility and scalability, providing a generalizable technical path and engineering reference for intelligent navigation and collaborative scheduling in complex maritime areas with high traffic density.

, correspAuthors=Hongkuo NIU, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, authorCompany=null, fund=null, authors=null, authorsList=Yunxiang LIU, Hongkuo NIU, Jianlin ZHU), CN=ArticleExt(id=1254362826156466844, articleId=1254362824688460436, tenantId=1146029695717560320, journalId=1254119036117037056, language=CN, title=基于ABiM-Ship的智能船舶轨迹预测, columnId=1254362825296634520, journalTitle=船舶工程, columnName=专题:智能船舶, runingTitle=null, highlight=null, articleAbstract=
[目的]

为提升船舶轨迹预测的精度与稳健性,

[方法]

提出ABiM-Ship网络,以双向选择性状态空间模型对历史航迹编码,利用注意力机制显式对齐航迹与航向、航速,设计2阶段端到端联合预测,先回归未来航迹与航向、航速,再以残差校正,引入Huber损失以约束物理误差并稳定收敛。

[结果]

试验表明:该网络在短、中、长程上的平均预测误差等指标表现均显著优于传统主流基线,预测精确度较高,且表征方法,2阶段结构与Huber损失均贡献显著增益。

[结论]

研究成果在保持线性时序复杂度的同时实现对轨迹、航向与航速的显式耦合与粗到细预测,具备良好的可复现与可推广性,可为复杂海域高密度交通的智能导航与协同调度提供可推广的技术路径与工程参考。

, correspAuthors=牛洪阔, authorNote=null, correspAuthorsNote=
牛洪阔(2000—),男,硕士研究生。研究方向:智能船舶航行轨迹预测、智能车辆行进轨迹预测、人工智能、机器学习等。E-mail:
, copyrightStatement=本刊已许可中国学术期刊(光盘版)电子杂志社在中国知网及其系列数据库产品中以数字化方式复制、汇编、发行、信息网络传播本刊全文,本刊著作权使用费与本刊稿酬一并支付;本刊所刊登文章均无知识产权争议,不涉及泄露国家秘密、技术秘密或商业秘密。作者向本刊提交文章发表的行为即视为同意我编辑部上述声明,文责自负。, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=hYlBlfb+oT4Pq4VLxVTwHQ==, magXml=RQl0FoiZvsIF1zE4ZCIUbw==, pdfUrl=null, pdf=hwQyJd9qkMDg0wu5mCdxLQ==, pdfFileSize=7667458, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=KR7Y2QbY+2j/s3eWYGbFUg==, mapNumber=null, authorCompany=null, fund=null, authors=

刘云翔(1964—),男,博士、教授、硕士研究生导师。研究方向:智能检测及控制技术、智能决策支持系统、智能信息处理、人工智能等。

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刘云翔(1964—),男,博士、教授、硕士研究生导师。研究方向:智能检测及控制技术、智能决策支持系统、智能信息处理、人工智能等。

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刘云翔(1964—),男,博士、教授、硕士研究生导师。研究方向:智能检测及控制技术、智能决策支持系统、智能信息处理、人工智能等。

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基于ABiM-Ship的智能船舶轨迹预测
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刘云翔 , 牛洪阔 * , 朱建林
船舶工程 | 专题:智能船舶 2026,48(3): 23-31
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船舶工程 | 专题:智能船舶 2026, 48(3): 23-31
基于ABiM-Ship的智能船舶轨迹预测
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刘云翔, 牛洪阔* , 朱建林
作者信息
  • 上海应用技术大学智能技术学部,上海 201418
  • 刘云翔(1964—),男,博士、教授、硕士研究生导师。研究方向:智能检测及控制技术、智能决策支持系统、智能信息处理、人工智能等。

通讯作者:

牛洪阔(2000—),男,硕士研究生。研究方向:智能船舶航行轨迹预测、智能车辆行进轨迹预测、人工智能、机器学习等。E-mail:
Intelligent Ship Trajectory Prediction Based on ABiM-Ship
Yunxiang LIU, Hongkuo NIU* , Jianlin ZHU
Affiliations
  • Faculty of Intelligence Technology, Shanghai Institute of Technology, Shanghai 201418, China
出版时间: 2026-03-25 doi: 10.13788/j.cnki.cbgc.2026.03.03
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[目的]

为提升船舶轨迹预测的精度与稳健性,

[方法]

提出ABiM-Ship网络,以双向选择性状态空间模型对历史航迹编码,利用注意力机制显式对齐航迹与航向、航速,设计2阶段端到端联合预测,先回归未来航迹与航向、航速,再以残差校正,引入Huber损失以约束物理误差并稳定收敛。

[结果]

试验表明:该网络在短、中、长程上的平均预测误差等指标表现均显著优于传统主流基线,预测精确度较高,且表征方法,2阶段结构与Huber损失均贡献显著增益。

[结论]

研究成果在保持线性时序复杂度的同时实现对轨迹、航向与航速的显式耦合与粗到细预测,具备良好的可复现与可推广性,可为复杂海域高密度交通的智能导航与协同调度提供可推广的技术路径与工程参考。

船舶自动识别系统  /  注意力机制  /  选择性结构状态空间模型  /  细化预测  /  轨迹预测
[Purpose]

To improve the accuracy and robustness of ship trajectory prediction,

[Method]

an ABiM-Ship network that encodes historical trajectories using a bidirectional selective state space model is proposed. An attention mechanism to explicitly align trajectories with heading and speed is utilized. A two-stage end-to-end joint prediction is designed, first regressing future trajectories, heading, and speed, then refining them using residual correction. Huber loss is introduced to constrain physical errors and stabilize convergence.

[Result]

The experimental results show that this network outperforms traditional mainstream baselines in terms of average prediction error over short, medium, and long distances, achieving high prediction accuracy. The representation method, two-stage structure, and Huber loss all contribute significantly to performance gains.

[Conclusion]

The research findings achieve explicit coupling and coarse-to-fine prediction for trajectories, heading, and speed while maintaining linear temporal complexity. They have good reproducibility and scalability, providing a generalizable technical path and engineering reference for intelligent navigation and collaborative scheduling in complex maritime areas with high traffic density.

automatic identification system  /  attention mechanism  /  selective state space model  /  prediction refine  /  trajectory prediction
刘云翔, 牛洪阔, 朱建林. 基于ABiM-Ship的智能船舶轨迹预测. 船舶工程, 2026 , 48 (3) : 23 -31 . DOI: 10.13788/j.cnki.cbgc.2026.03.03
Yunxiang LIU, Hongkuo NIU, Jianlin ZHU. Intelligent Ship Trajectory Prediction Based on ABiM-Ship[J]. Ship Engineering, 2026 , 48 (3) : 23 -31 . DOI: 10.13788/j.cnki.cbgc.2026.03.03
  • 国家自然科学基金委员会资助项目(61976140)
2026年第48卷第3期
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文章信息
doi: 10.13788/j.cnki.cbgc.2026.03.03
  • 接收时间:2025-08-21
  • 首发时间:2026-04-24
  • 出版时间:2026-03-25
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出版历史
  • 收稿日期:2025-08-21
  • 修回日期:2025-10-18
基金
国家自然科学基金委员会资助项目(61976140)
作者信息
    上海应用技术大学智能技术学部,上海 201418

通讯作者:

牛洪阔(2000—),男,硕士研究生。研究方向:智能船舶航行轨迹预测、智能车辆行进轨迹预测、人工智能、机器学习等。E-mail:
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2种不同金属材料的力学参数

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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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